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HybrInt – Hybrid Intelligence through Interpretable AI

The aim of this project is to strengthen basic AI research jointly at Leibniz University Hannover and Osnabrück University in the name of hybrid intelligence. The key idea is to combine the strengths of the complementary heterogeneous intelligence of humans and machine: human intelligence is defined by the ability to learn, reason, and interact with the environment based on their knowledge, whereas AI is attributed to machines. This includes tasks, such as language processing, object recognition, model building, and applying that knowledge to solve problems.

With the overarching goal, the research is structured by seven subprojects (SP) ranging from basic research to application and hardware. These project subsume research on knowledge graphs, explainable AI, resource-efficient hardware acceleration, reinforcement learning, trustworthiness, robotics, and human-centered explanations.

• SP1: Knowledge Graph-based Extraction of Research Knowledge from Articles;
• SP2: Knowledge-Graph-Based Reinforcement Learning;
• SP3: Knowledge Graph-based Interpretable Learning on Complex Data;
• SP4: Algorithm Hardware Codesign for Resource-Efficient Interpretable AI Methods;
• SP5: Robust Online Single Plant Classification from Multimodal Sensor Data Including Semantic Context Knowledge;
• SP6: Credible and Structured Interpretations of Machine Learning Models;
• SP7: Human-Centered Explanation of Machine Learning Results.

The envisaged research cooperation should manifest itself in two joint use cases in the field of high precision farming.

1. Optimizing Irrigation – Agricultural Water Management. We aim to develop novel methods, where expert knowledge on irrigation optimization can be incorporated in a human-understandable fashion and new knowledge can be extracted from the learning agent’s experience to enrich human expertise.
2. Agricultural Research Knowledge Observatory. We aim to retrieve relevant literature addressing biodiversity, agricultural, and plant-related knowledge questions, and to create structured contribution descriptions for each of the found articles. In addition, we also aim to link the literature to relevant datasets and possibly other artifacts such as images, videos etc.